The increasing demand for power distribution systems in terms of control with nearly immediate response requires deploying a new type of user equipment (UE) that demands permanent connectivity. In Narrowband Internet of Things (NB-IoT) systems, the traffic generated by such UEs may constitute a large part of the overall load. In this article, we first propose a detailed 2-D Markov chain model to capture the system’s behavior with the mixture of conventional stochastic and regular traffic types. To provide a computationally efficient solution, we then apply the state aggregation technique to reduce it to a 1-D model and develop approximations and associated numerical algorithms for assessing the mean delay when transmitting the considered traffic. Our results show that a single NB-IoT cell remains stable for up to$72\times {}10^{4}$conventional UEs and$9\times {}10^{3}$UEs demanding permanent connectivity. The presence of the latter UEs type has a linear effect on their delay, but affects conventional UEs more drastically. A delay bound of 10 s specified in ITU-R M.2410 is met for the conventional UEs, even under a high number of permanently connected UEs$({10^{3}})$. However, the delay on the side of the latter UEs is violated even for 100 permanently connected UEs requiring redesigning the NB-IoT channel access mechanism or expanding resources.

In mobile network operations, managing Key Performance Indicators (KPIs), like user data rates, to ensure satisfactory Quality of Service ($\mathbf{Q o S}$) is crucial. Terrestrial cellular networks often struggle to deliver adequate data rates in remote regions with insufficient infrastructure or in densely populated urban areas with high user concentration. This presents a significant challenge in maintaining high-quality network service across diverse environments. Non-Terrestrial Networks (NTNs), utilizing Unmanned Aerial Vehicle Base Stations (UAV-BSs), with their ability to move and be deployed flexibly, present a viable solution for enhancing data rates and improving overall network performance. Despite their potential, the challenge lies in effectively positioning UAV-BSs within these NTNs to maximize data transmission rates while managing operational constraints such as battery life and interference caused by proximity between UAV-BSs. To effectively manage these challenges, this paper introduces a realistic optimization framework that combines Constrained K-means clustering with the Particle Swarm Optimization (PSO) algorithm for optimizing the positioning of UAVBSs in NTNs. This strategy considers operational constraints and the necessity for nearby recharging stations. Our simulation results show notable improvements in average data rates for mobile users compared to conventional approaches, such as PSO-based UAV-BS positions. Through comparative analysis, this study underscores significant improvements in network performance, demonstrating the effectiveness of our resource optimization strategy in modern NTNs.

Edge-to-cloud computing is an emerging paradigm for distributing computational tasks between edge devices and cloud resources. Different approaches for orchestration, offloading, and many more purposes have been introduced in research. However, it is still not clear what has been implemented in the industry. This work aims to merge this gap by mapping the existing knowledge on edge-to-cloud tools by providing an overview of the current state of research in this area and identifying research gaps and challenges. For this purpose, we conducted a Multivocal Literature Review (MLR) by analyzing 40 tools from 1073 primary studies (220 PS from the white literature and 853 PS from the grey literature). We categorized the tools based on their characteristics and targeted environments. Overall, this systematic mapping study provides a comprehensive overview of edge-to-cloud tools and highlights several opportunities for researchers and practitioners for future research in this area. Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.

Various publicly available studies show that millions of cybersecurity experts are missing worldwide. One possible way to tackle the workforce gap is with tailored higher education programmes. The goal of this paper is to present the relevant projects and frameworks of the European Union which can guide the development of novel cybersecurity education offerings. We describe the most relevant and freely available tools and test them in the development of a joint Master study programme to be offered by a consortium of five European universities. We show that these tools allow educators and study programme developers to map their outputs to the European Cybersecurity Framework developed by the ENISA and other similar frameworks. We complete our work with a detailed analysis of a joint cybersecurity master programme consisting of four innovative and distinctly different tracks.

Natural disasters and other emergencies can disrupt terrestrial communication infrastructure, leaving first responders and affected people without reliable connectivity. Unmanned Aerial Vehicles (UAVs) have been proposed as a promising technology for providing on-demand connectivity in such situations, but limited battery capacity remains a significant challenge. As a possible solution, Tethered UAVs (TUAVs) can extend the operation time and ensure seamless service due to their fixed connection to a ground station. In this paper, we propose a novel approach to TUAV deployment that involves using a robotic car to carry the necessary equipment for communication establishment and enable remote control by a human operator. We focus on finding the optimal locations for TUAV deployment to ensure complete user coverage and permanent connections between neighboring TUAVs. We utilize the Genetic algorithm to solve the problem of TUAV placement, considering constraints such as signal propagation models and coverage radius. The air-to-ground and air-to-air channel modeling determines the optimal perpendicular height and coverage radius of TUAVs. Our Matlab simulation results demonstrate that our proposed approach offers efficient and optimal solutions for TUAV placement using GA. This research has significant implications for disaster management and emergency response, providing a reliable and effective communication infrastructure in critical situations. Our findings can support the development of disaster response strategies and policies that incorporate TUAVs as a crucial component for on-demand connectivity solutions in disaster-affected areas. The proposed TUAV deployment approach is expected to have broad applications beyond disaster management in remote sensing, surveillance, and search and rescue operations.

Unmanned aerial vehicles (UAVs) have attracted significant attention. Many potential applications are predicted to bring abundant business opportunities in the next decade thanks to the ubiquitous connectivity of the 4G and 5G cellular networks. Unlike terrestrial deployments, airborne infrastructures have diverse attributes, e.g., increased deployment flexibility, line-of-sight (LoS) connections, and controlled mobility. These attributes bring new research questions and challenges, including LoS dominant ground channels together with increased aerial-terrestrial network interference, the exploitation of the degree of freedom (DoF) provided by the UAV mobility, and the different and tighter quality of service (QoS) requirements for purposes of unmanned traffic management (UTM). Further challenges stem from existing infrastructure oriented towards service availability in low altitudes and low data transfer costs. A key challenge in this context is to achieve and guarantee sufficient service reliability in all altitudes where UTM is planned to be used. Obtained results from the measurement campaign at Twente airport indicate the performance of the 4G cellular network is stable and equals the terrestrial use cases up to 60 m of altitude. However, if the altitude increases more, the network performance degrades significantly. In detail, the analysis revealed the fact the performance drop is significant, especially for downlink transmissions (from ground to UAVs), as the strong interference and connection outages were observed in increased numbers.

During the past years, the LoRaWAN technology took a prominent position among the wireless connectivity solutions for the Internet of Things (IoT) and has attracted substantial attention. The LoRaWAN technology has become popular for collecting the data from the sensors, which traditionally have periodic communication patterns. Meanwhile, recent studies have shown that the LoRaWAN procedures, such as the over-the-air activation, may also compromise the uniformity of data traffic distribution. Therefore, in this study, we investigate how the communication pattern of LoRaWAN devices during activation and data communication affects the overall network performance with respect to speed of activation and overall packet delivery probability. We show that the periodic communication patterns, widely employed by commercial LoRaWAN devices today, are less efficient than the patterns featuring random delays between the packets. Also, we find that introduction of a random delay between uplink data packets helps randomizing the channel access and enables network performance boost both for the application data transfer and during the activation. Finally, we show that implementation of the suggested communication patterns modification is feasible for the state-of-the-art LoRaWAN transceivers with no hardware modifications.

The new types of industry-driven applications that need to be supported by low-power wide-area networks (LP-WANs), such as remote control or metering of devices within the massive machine-type infrastructures (e.g., Smart Grids), require a permanent connection to the remote server. In addition, there is also a shift in the communication paradigm, as the user equipment (UE) nodes are queried in regular and frequent intervals. Notably, the presence of this type of traffic may drastically deteriorate the performance of LPWAN technologies initially developed to support conventional use-cases characterized by non-synchronized transmissions. Though none of the LPWAN technologies is inherently designed to handle such demanding communication patterns, the narrowband Internet of things (NB-IoT) still stands for the best candidate as it operates within the license frequency spectrum. To optimize the delay performance of both types of traffic coexisting at the NB-IoT air interface, we propose an approach based on spreading the message transmission time instants of regular and stochastic traffic. We show an optimal value of the spreading interval minimizing the message transmission delay of regular traffic and propose a mathematical model to estimate its value. By parameterizing the model using a detailed measurements campaign of NB-IoT, we show that the optimal value of spreading interval and associated mean message delay is a linear function of the number of UEs. We report these values for a wide range of UEs in the coverage area of the NB-IoT base station and show that conventional stochastic traffic does not influence regular traffic performance.

An assessment of radio network coverage, usually in the form of a measurement campaign, is essential for multibase-station (multi-BS) network deployment and maintenance. It can be conducted by a network operator or its served consumers. However, the number of measurement points and their locations may not be known in advance for an efficient and accurate evaluation. The main goal of this study is to propose a new methodology for understanding the selection of measurement points during coverage and signal quality assessment. It is particularly tailored to multi-BS low-power wide-area network (LPWAN) deployments without explicit knowledge of BS locations. To this aim, we first conduct a large-scale measurement campaign for three popular LPWAN technologies, namely, narrowband IoT (NB-IoT), Sigfox, and LoRaWAN. Utilizing this baseline data, we develop a procedure for identifying the minimum set of measurement points for the coverage assessment with a given accuracy as well as study which interpolation algorithms produce the lowest approximation error. Our results demonstrate that a random choice of measurement points is on par with their deterministic selection. Out of the candidate interpolation algorithms, the Kriging method offers attractive performance in terms of the absolute error for NB-IoT deployments. In contrast, for Sigfox and LoRaWAN infrastructures, less complex techniques, such as the natural neighbor, linear interpolation, or inverse-distance weighting, can achieve comparable (and occasionally even better) accuracy levels.

Considering the dynamically changing nature of the radio propagation environment, the envisioned battery lifetime of the end device (ED) for massive machine-type communication (mMTC) stands for a critical challenge. As the selected radio technology bounds the battery lifetime, the possibility of choosing among several low-power wide-area network (LPWAN) technologies integrated at a single ED may dramatically improve its lifetime. In this article, we propose a novel approach of battery lifetime extension utilizing reinforcement learning (RL) policies. Notably, the system assesses the radio environment conditions and assigns the appropriate rewards to minimize the overall power consumption and increase reliability. To this aim, we carry out extensive propagation and power measurements campaigns at the city-scale level and then utilize these results for composing real-life use cases for static and mobile deployments. Our numerical results show that RL-based techniques allow for a noticeable increase in EDs’ battery lifetime when operating in the multi-RAT mode. Furthermore, out of all considered schemes, the performance of the weighted average policy shows the most consistent results for both considered deployments. Specifically, all RL policies can achieve 90% of their maximum gain during the initialization phase for the stationary EDs while utilizing less than 50 messages. Considering the mobile deployment, the improvements in battery lifetime could reach 200%.

Extended Reality (XR) is a disruptive technology that will play an essential role in future society by creating an immersive human–machine interface. For their mass adoption, XR head–mounted devices have to be made light and sleek in design, which may require distributed computing capabilities, where high-end devices wirelessly offload computational tasks to the accompanying processing units. To satisfy demanding wireless connectivity requirements of the emerging XR applications, the devices are expected to rely on radio technologies that operate in millimeter-wave (mmWave) frequency range and require directional transmission. In this paper, we evaluate a dynamic system of distributed 3D wearable networks operating in the mmWave band. We provide closed-form expressions for the session drop probability, the mean number of sessions that can run simultaneously, and its lower bound, which aid in understanding the impact of different parameters on the coexistence of dense directional wearable networks in the 3D space.

Technology is continually undergoing a constituent development caused by the appearance of billions new interconnected “things” and their entrenchment in our daily lives. One of the underlying versatile technologies, namely wearables, is able to capture rich contextual information produced by such devices and use it to deliver a legitimately personalized experience. The main aim of this paper is to shed light on the history of wearable devices and provide a state-of-the-art review on the wearable market. Moreover, the paper provides an extensive and diverse classification of wearables, based on various factors, a discussion on wireless communication technologies, architectures, data processing aspects, and market status, as well as a variety of other actual information on wearable technology. Finally, the survey highlights the critical challenges and existing/future solutions.

In this paper, we study real-world performance of Sigfox, which is one of the most mature Low-Power Wide-Area Network (LPWAN) technologies that operate in unlicensed frequency bands. During an extensive measurement campaign conducted over three months in the city of Brno, Czech Republic, we assessed the communication performance and the radio channel properties in 311 different test locations. We observed that despite the challenging natural landscape and urban environment of the test area, more than 94% of the packets sent were received successfully, with at least one packet delivered from 297 out of 311 tested locations. Our results also reported experiment-based radio channel and signal-to-noise characterization as well as provided insights into the efficiency of two crucial mechanisms used by Sigfox to improve the packet delivery - packet repetition and multi-gateway reception. Finally, we employed our experimental data to understand the efficiency of two non-fingerprint localization methods based on received signal strength indicator in a practical Sigfox network.

A remarkable progress in the Low Power Wide Area Network (LPWAN) technologies over the recent years opens new opportunities for developing versatile massive Internet of Things (IoT) applications. In this paper, we focus on one of the most popular LPWAN technologies operating in the license-exempt frequency bands, named LoRaWAN. The key contribution of this study is our unique set of results obtained during an extensive measurement campaign conducted in the city of Brno, Czech Republic. During a three-months-period, the connectivity of a public Long Range Wide Area Network (LoRaWAN) with more than 20 gateways (GWs) was assessed at 231 test locations. This paper presents an analysis of the obtained results, aimed at capturing the effects related to the spatial diversity of the GW locations and the real-life multi-GW network operation with all its practical features. One of our findings is the fact that only for 47% tested locations the GW featuring the minimum geographical distance demonstrated the highest received signal strength and signal-to-noise ratio (SNR). Also, our results captured and characterized the variations in the received signal strength indicator (RSSI) and SNR as a function of the communication distance in an urban environment, and illustrated the distribution of the spreading factors (SFs) as a result of the adaptive data rate (ADR) algorithm operation in a real-life multi-GW deployment.

Providing sufficient mobile coverage during mass public events or critical situations is a highly challenging task for the network operators. To fulfill the extreme capacity and coverage demands within a limited area, several augmenting solutions might be used. Among them, novel technologies like a fleet of compact base stations mounted on Unmanned Aerial Vehicles (UAVs) are gaining momentum because of their time- and cost- efficient deployment. Despite the fact that the concept of aerial wireless access networks has been investigated recently in many research studies, there are still numerous practical aspects that require further understanding and extensive evaluation. Taking this as a motivation, in this paper, we develop the concept of continuous wireless coverage provisioning by the means of UAVs and assess its usability in mass scenarios with thousands of users. With our system-level simulations as well as a measurement campaign, we take into account a set of important parameters including weather conditions, UAV speed, weight, power consumption, and millimeter- wave (mmWave) antenna configuration. As a result, we provide more realistic data about the performance of the access and backhaul links together with the practical lessons learned about the design and real-world applicability of the UAV-enabled wireless access networks.

The landscape of the contemporary IoT radio access technologies (RATs) is excessively diverse, especially when it comes to such a complex environment as Smart City. On the one hand, this diversity offers operators sufficient flexibility to select the most appropriate RAT for their target application. On the other hand, it becomes a severe limiting factor leading to high level of uncertainty for the IoT device vendors, who need to decide, which technology to support in their hardware. In this paper, we consider the provisioning of the low-power wide area network (LPWAN) devices supporting multiple RATs. First, we briefly discuss the parameters of several potential radio technologies as well as analyze the pros and cons of combining them in a single device. Next, we prototype a real-life device capable of communicating via two perspective LPWAN technologies, namely, LoRaWAN and NB-IoT, and report on the initial results of its performance evaluation. These confirm the feasibility of instrumenting dual-mode devices as well as reveal several important aspects related to the development of multi-radio IoT equipment and its performance. In our view, due to their higher flexibility, reliability, and dependability, the devices such as the one developed can be beneficial for various Smart City applications, with smart energy grids and road traffic control being only two of many examples.

Internet access has become commonplace in the modern world. As the number of users and the amount of data traffic in the Internet keep rising exponentially, while the requirements of novel applications are becoming more stringent, there is a clear need for new networking solutions. Therefore, one of the key concepts in resolving the challenges of the upcoming 5G era of communications will be represented by multi-radio heterogeneous networks, where the users can gain benefits by either being connected to multiple different radio technologies simultaneously or seamlessly changing from one network to another based on their needs. In this work, we propose a multi-purpose automated vehicular platform prototype equipped with multiple radio access technologies, which was constructed to demonstrate the potential performance gains provided by the use of multi-radio heterogeneous networks in terms of network throughput, latency, and reliability. We discuss the potential drawbacks of using multiple radio interfaces at the same time. The constructed vehicular platform prototype constitutes a flexible research framework for communications technology within heterogeneous networks and becomes helpful for supporting future use cases of industrial IoT applications.

A diversity of wireless technologies will collaborate to support the fifth‐generation (5G) communication networks with their demanding applications and services. Despite decisive progress in many enabling solutions, next‐generation cellular deployments may still suffer from a glaring lack of bandwidth due to inefficient utilization of radio spectrum, which calls for immediate action. To this end, several capable frameworks have recently emerged to all help the mobile network operators (MNOs) leverage the abundant frequency bands that are utilized lightly by other incumbents. Along these lines, the recent Licensed Shared Access (LSA) regulatory framework allows for controlled sharing of spectrum between an incumbent and a licensee, such as the MNO, which coexist geographically. This powerful concept has been subject to several early technology demonstrations that confirm its implementation feasibility. However, the full potential of LSA‐based spectrum management can only become available if it is empowered to operate dynamically and at high space‐time‐frequency granularity. Complementing the prior efforts, we in this work outline the functionality that is required by the LSA system to achieve the much needed flexible operation as well as report on the results of our respective live trial that employs a full‐fledged commercial‐grade cellular network deployment. Our practical results become instrumental to facilitate more dynamic bandwidth sharing and thus promise to advance on the degrees of spectrum utilization in future 5G systems without compromising the service quality of their users.

Owing to a steadily increasing demand for efficient spectrum utilization as part of the fifth‐generation (5G) cellular concept, it becomes crucial to revise the existing radio spectrum management techniques and provide more flexible solutions for the corresponding challenges. A new wave of spectrum policy reforms can thus be envisaged by producing a paradigm shift from static to dynamic orchestration of shared resources. The emerging Licensed Shared Access (LSA) regulatory framework enables flexible spectrum sharing between a limited number of users that access the same frequency bands, while guaranteeing better interference mitigation. In this work, an advanced user satisfaction‐aware spectrum management strategy for dynamic LSA management in 5G networks is proposed to balance both the connected user satisfaction and the Mobile Network Operator (MNO) resource utilization. The approach is based on the MNO decision policy that combines both pricing and rejection rules in the implemented processes. Our study offers a classification built over several types of users, different corresponding attributes, and a number of MNO’s decision scenarios. Our investigations are built on Criteria‐Based Resource Management (CBRM) framework, which has been specifically designed to facilitate dynamic LSA management in 5G mobile networks. To verify the proposed model, the results (spectrum utilization, estimated Secondary User price for the future connection, and user selection methodology in case of user rejection process) are validated numerically as we yield important conclusions on the applicability of our approach, which may offer valuable guidelines for efficient radio spectrum management in highly dynamic and heterogeneous 5G environments.

As next-generation mobile networks are rapidly taking shape driven by the target standardization requirements and initial trial implementations, a range of accompanying technologies prepare to support them with more reliable wireless access and improved service provisioning. Among these are more advanced spectrum sharing options enabled by the emerging Licensed Shared Access (LSA) regulatory framework, which aims to efficiently employ the capacity of underutilized frequency bands in a controlled manner. The concept of LSA promises to equip network operators with the much needed additional spectrum on the secondary basis and thus brings changes to the existing cellular network management. Hence, additional research is in prompt demand to determine the required levels of Quality of Service (QoS) and service provisioning reliability, especially in cases of dynamic geographical and temporal LSA sharing. Motivated by this recent urge and having at our disposal a fully-functional 3GPP LTE cellular deployment, we have committed to implement and trial the principles of dynamic LSA-compatible spectrum management. This paper is our first disclosure on the comprehensive experimental evaluation of this promising technology. We expect that these unprecedented practical results together with the key lessons learned will become a valuable reference point for the subsequent integration of flexible LSA-based services, suitable for inter-operator and multi-tenant spectrum sharing.

The IEEE 802.11 standard defines access categories (AC) and differentiated medium access control mechanisms for wireless local area networks. The preferential or deferral treatment of frames is achieved using configurable Arbitration Inter-Frame Spaces (AIFS) and customizable Contention Window (CW) sizes. In this paper, we address the problem of determining when a station, being a part of wireless communication, will access the medium. We present an algorithm calculating the probability of winning the contention by a given station, characterized by its AIFS and CW values. The probability of collision is calculated by similar means. The results were verified by simulations in Matlab and OPNET Modeler tools. We also introduce a web applet implementing and interactively demonstrating the results.

Intensive competition between network operators as well as steady increase in mobile traffic call for additional investments into the networking infrastructure. Keeping current mobile networks profitable, the following criteria should be satisfied: end-user quality expectations need to be fulfilled on the one hand and service quality overprovisioning should be eliminated on the other. This generates growing demand for adequate QoE estimation models accounting for dominant mobile data services. Moreover, novel models have to fulfill requirements of good applicability and high accuracy. Our approach in this paper details an advanced QoE estimation model, extensively verified with appropriate statistical tools, for the most popular mobile web services: browsing, download, and upload. The proposed model follows from a recent extensive QoE assessment and utilizes the bitrate together with the initial loading delay as well-measurable input parameters. Targeted to estimate the mean opinion score, our proposed model demonstrates an excellent convergence across the considered practical scenarios.